Baillet Sylvain

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infar...

To compare predicted and final infarct lesion volumes determined by processing apparent diffusion coefficient (ADC) maps derived at admission diffusion-weighted (DW) magnetic resonance (MR) imaging in patients with acute stroke and to verify that predicted areas of infarct growth reflect at-risk penumbral regions based on recanalization status.

– BrainStorm is a collaborative project to build a software suite for EEG and MEG data visualization, mod-eling, and source imaging, with integration of MRI and fMRI information. BrainStorm is a Matlab-based toolbox distributed under the GNU public licensing and runs on any ...

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varyi...

The hippocampus (Hc) and the amygdala (Am) are two cerebral structures that play a central role in main cognitive processes. Their segmentation allows atrophy in specific neurological illnesses to be quantified, but is made difficult by the complexity of the structures. In this work, a new algorithm for the simultaneous segmentation of Hc and Am based on competitive homotopic region deformations is presented. The deformations are constrained by relational priors derived from anatomical knowledge, namely probabilities for each structure around automatically retrieved landmarks at the border of the objects. The approach is designed to perform well on data from diseased subjects. The segmentation is initialized by extracting a bounding box and positioning two seeds; total execution time for both sides is between 10 and 15 minutes including initialization for the two structures. We present the results of validation based on comparison with manual segmentation, using volume error, spatial overlap and border distance measures. For 8 young healthy subjects the mean volume error was 7% for Hc and 11% for Am, the overlap: 84% for Hc and 83% for Am, the maximal distance: 4.2mm for Hc and 3.1mm for Am; for 4 Alzheimer's disease patients the mean volume error was 9% for Hc and Am, the overlap: 83% for Hc and 78% for Am, the maximal distance: 6mm for Hc and 4.4mm for Am. We conclude that the performance of the proposed method compares favourably with that of other published approaches in terms of accuracy and has a short execution time.

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infar...

We describe a new algorithm for the automated segmentation of the hippocampus (Hc) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the au...